Abstract: Some important parameters testing have certain error which brings some difficulty to ensure monitoring the production process and the real-time control of some important quality parameters.Because of the error data may be contained in the independent variables and dependent variables of the sample data,which may affect the accuracy of the model,so in this article we use the weighted least-square algorithm to give the punishment of square-errors of each sample different weights to overcome the abnormal influence of the training samples.Using the simulation data from the Pensim simulation platform to establish the weighted least squares vector machine(WLS-SVM)model in the Penicillin Fermentation by using particle swarm optimization(PSO)on the weighted least squares vector machine parameters optimization algorithm,through the simulation experiments show that the algorithm is used for the effectiveness of penicillin fermentation process modeling.

Key words: weighting, the least square support vector machine, model, penicillin

摘要： 青霉素发酵过程中,一些重要参数的检测存在一定的误差,给生产过程的监测及重要参数的实时监控等带来一定困难。样本数据中自变量、因变量均有可能包含误差数据,影响模型建立的准确性,本文采用加权最小二乘算法,给各个样本的误差平方赋予不同权重用于克服异常训练样本的影响,利用Pensim仿真平台数据,采用粒子群算法(PSO)对加权最小二乘向量机算法(WLS-SVM)的参数寻优,建立青霉素发酵过程模型,通过仿真实验表明了该算法用于青霉素发酵过程建模的有效性。

关键词: 加权, 最小二乘支持向量机, 建模, 青霉素